





'''simple docstring'''


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
a__			:					Dict       						=						logging.get_logger(__name__)

a__			:					Any       						=						{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
a__			:					Tuple       						=						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Optional[Any]			,	SCREAMING_SNAKE_CASE_ :			Union[str, Any]			,	SCREAMING_SNAKE_CASE_ :			List[str]			,	SCREAMING_SNAKE_CASE_ :			Any			,	SCREAMING_SNAKE_CASE_ :			Optional[int] )							->     Any:

       """simple docstring"""


       for attribute in key.split('''.''' ):
              UpperCAmelCase  =						getattr(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )

       if weight_type is not None:
              UpperCAmelCase  =						getattr(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ).shape
       else:
              UpperCAmelCase  =						hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
                  f" {value.shape} for {full_name}" )

       if weight_type == "weight":
              UpperCAmelCase  =						value
       elif weight_type == "weight_g":
              UpperCAmelCase  =						value
       elif weight_type == "weight_v":
              UpperCAmelCase  =						value
       elif weight_type == "bias":
              UpperCAmelCase  =						value
       else:
              UpperCAmelCase  =						value

       logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Union[str, Any]			,	SCREAMING_SNAKE_CASE_ :			str )							->     List[Any]:

       """simple docstring"""


       UpperCAmelCase  =						[]
       UpperCAmelCase  =						fairseq_model.state_dict()

       UpperCAmelCase  =						hf_model.unispeech_sat.feature_extractor

       for name, value in fairseq_dict.items():
              UpperCAmelCase  =						False
              if "conv_layers" in name:
                     load_conv_layer(
                         SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	hf_model.config.feat_extract_norm == '''group'''			,	)
                     UpperCAmelCase  =						True
              else:
                     for key, mapped_key in MAPPING.items():
                            UpperCAmelCase  =						'''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                            if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
                                   if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
                                          # special case since naming is very similar
                                          continue
                                   UpperCAmelCase  =						True
                                   if "*" in mapped_key:
                                          UpperCAmelCase  =						name.split(SCREAMING_SNAKE_CASE_ )[0].split('''.''' )[-2]
                                          UpperCAmelCase  =						mapped_key.replace('''*'''			,	SCREAMING_SNAKE_CASE_ )
                                   if "weight_g" in name:
                                          UpperCAmelCase  =						'''weight_g'''
                                   elif "weight_v" in name:
                                          UpperCAmelCase  =						'''weight_v'''
                                   elif "bias" in name:
                                          UpperCAmelCase  =						'''bias'''
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          UpperCAmelCase  =						'''weight'''
                                   else:
                                          UpperCAmelCase  =						None
                                   set_recursively(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )
                            continue
              if not is_used:
                     unused_weights.append(SCREAMING_SNAKE_CASE_ )

       logger.warning(f"Unused weights: {unused_weights}" )
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Any			,	SCREAMING_SNAKE_CASE_ :			Any			,	SCREAMING_SNAKE_CASE_ :			Union[str, Any]			,	SCREAMING_SNAKE_CASE_ :			List[str]			,	SCREAMING_SNAKE_CASE_ :			Dict )							->     Optional[int]:

       """simple docstring"""


       UpperCAmelCase  =						full_name.split('''conv_layers.''' )[-1]
       UpperCAmelCase  =						name.split('''.''' )
       UpperCAmelCase  =						int(items[0] )
       UpperCAmelCase  =						int(items[1] )

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                f"{full_name} has size {value.shape}, but"
                                f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." )
                     UpperCAmelCase  =						value
                     logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                f"{full_name} has size {value.shape}, but"
                                f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." )
                     UpperCAmelCase  =						value
                     logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                f"{full_name} has size {value.shape}, but"
                                f" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found." )
                     UpperCAmelCase  =						value
                     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                f"{full_name} has size {value.shape}, but"
                                f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found." )
                     UpperCAmelCase  =						value
                     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
       else:
              unused_weights.append(SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Optional[int]			,	SCREAMING_SNAKE_CASE_ :			List[str]			,	SCREAMING_SNAKE_CASE_ :			int=None			,	SCREAMING_SNAKE_CASE_ :			Any=None			,	SCREAMING_SNAKE_CASE_ :			Union[str, Any]=True )							->     Optional[Any]:

       """simple docstring"""


       if config_path is not None:
              UpperCAmelCase  =						UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
       else:
              UpperCAmelCase  =						UniSpeechSatConfig()

       UpperCAmelCase  =						''''''

       if is_finetuned:
              UpperCAmelCase  =						UniSpeechSatForCTC(SCREAMING_SNAKE_CASE_ )
       else:
              UpperCAmelCase  =						UniSpeechSatForPreTraining(SCREAMING_SNAKE_CASE_ )

       UpperCAmelCase,					UpperCAmelCase,					UpperCAmelCase  =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]			,	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
       UpperCAmelCase  =						model[0].eval()

       recursively_load_weights(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )

       hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
    a__			:					int       						=						argparse.ArgumentParser()
    parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
    parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
    parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    parser.add_argument(
        '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
    )
    a__			:					Dict       						=						parser.parse_args()
    convert_unispeech_sat_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCAmelCase_     						=				{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				['''GPTSw3Tokenizer''']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_swa import GPTSwaTokenizer

else:
    import sys

    lowerCAmelCase_     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


